Density-mediated carry-over effects explain variation in breeding output across time in a seasonal population
Bibliographic record
Abstract
In seasonal environments, where density dependence can operate throughout the annual cycle, vital rates are typically considered to be a function of the number of individuals at the beginning of each season. However, variation in density in the previous season could also cause surviving individuals to be in poor physiological condition, which could carry over to influence individual success in the following season. We examine this hypothesis using replicated populations of Drosophila melanogaster, the common fruitfly, over 23 non-overlapping generations with distinct breeding and non-breeding seasons. We found that the density at the beginning of the non-breeding season negatively affected the fresh weight of individuals that survived the non-breeding season and resulted in a 25% decrease in per capita breeding output among those that survived to the next season to breed. At the population level, per capita breeding output was best explained by a model that incorporated density at the beginning of the previous non-breeding season (carry-over effect, COE) and density at the beginning of the breeding season. Our results support the idea that density-mediated COEs are critical for understanding population dynamics in seasonal environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".